Texture removal by pixel classification using a rotating filter

Magnier, Baptiste; Montesinos, Philippe; Diep, Daniel

doi:10.1109/icassp.2011.5946599

Cited by 11 publications

(22 citation statements)

References 12 publications

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“…This technique is very attractive because of the simplicity of the filters and the intelligibility of their functioning, which helps finding perceptuel features close to human vision in images. These rotating filters have been used in a number of applications such as edge and ridges detection, texture suppression or image regularization [17,14,13,12,15]. In the method presented in this paper, a smoothing rotating filter first enables classification between edges and homogeneous regions, so that either isotropic or anisotropic smoothing can be used.…”

Section: Discussionmentioning

confidence: 99%

“…2) in order to build a signal s, which is a function of a rotation angle θ and the underlying signal. As shown in [17,14,12,13], smoothing with rotating filters implies that the image is smoothed with a bank of rotated anisotropic Gaussian half-kernels:…”

Section: A Rotating Smoothing Half-filtermentioning

confidence: 99%

“…a square is transformed into a circle after a certain number of iterations. In order to minimize this effect, we consider in [14] both directions provided by the curvature of the signal s(θ) (see Section 3.3) only in image areas where flatness has been detected (Fig. 4(c)).…”

Section: Diffusion Scheme In Two Positive Directionsmentioning

confidence: 99%

“…This paper introduces a rotating filter (inspired by [17,14,13]) able to detect homogenous regions and edges regions, even in images containing heavy noise. Then, it presents an anisotropic edge detector capable of defining two directions for pixels belonging to edges.…”

mentioning

confidence: 99%

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Perceptual Color Image Smoothing via a New Region-Based PDE Scheme

Magnier

Montesinos

Diep

2013

ELCVIA

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In this paper, we present a new color image regularization method using a rotating smoothing filter. This approach combines a pixel classification method, which roughly determines if a pixel belongs to a homogenous region or an edge with an anisotropic perceptual edge detector capable of computing two precise diffusion directions. Using a now classical formulation, image regularization is here treated as a variational model, where successive iterations of associated PDE (Partial Differential Equation) are equivalent to a diffusion process. Our model uses two kinds of diffusion: isotropic and anisotropic diffusion. Anisotropic diffusion is accurately controlled near edges and corners, while isotropic diffusion is applied to smooth regions either homogeneous or corrupted by noise. A comparison of our approach with other regularization methods applied on real images demonstrate that our model is able to efficiently restore images as well as handle diffusion, and at the same time preserve edges and corners well.

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Section: Discussionmentioning

confidence: 99%

Section: A Rotating Smoothing Half-filtermentioning

confidence: 99%

Section: Diffusion Scheme In Two Positive Directionsmentioning

confidence: 99%

mentioning

confidence: 99%

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Perceptual Color Image Smoothing via a New Region-Based PDE Scheme

Magnier

Montesinos

Diep

2013

ELCVIA

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“…Indeed, anisotropic filters provide good results and are often used in edge detection [6] [7], texture removal [8], image enhancing and restoration [9]. In several domains, anisotropic filters make possible better robustness than classical method.…”

Section: Introductionmentioning

confidence: 99%

Perceptual curve extraction

Magnier

Diep

Montesinos

2011

2011 IEEE 10th IVMSP Workshop: Perception and Visual Signal Analysis

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In this paper we propose a new perceptual curve detection method in images based on the difference of half rotating Gaussian filters. The novelty of this approach resides in the mixing of ideas coming both from directional filters, perceptual organization and DoG method. We obtain a new anisotropic DoG detector enabling very precise detection of perceptual curve points. Moreover, this detector performs correctly at perceptual curves even if highly bended, and is precise on perceptual junctions. This detector has been tested successfully on various image types presenting real difficult problems for classical detection methods.

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Derivative Half Gaussian Kernels and Shock Filter

Magnier¹,

Noblet²,

Voisin³

et al. 2018

Advanced Concepts for Intelligent Vision Systems

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Shock filter represents an important family in the field of nonlinear Partial Differential Equations (PDEs) models for image restoration and enhancement. Commonly, the smoothed second order derivative of the image assists this type of method in the deblurring mechanism. This paper presents the advantages to insert information issued of oriented half Gaussian kernels in a shock filter process. Edge directions assist to preserve contours whereas the gradient direction allow to enhance and deblur images. For this purpose, the two edge directions are extracted by the oriented half kernels, preserving and enhancing well corner points and object contours as well as small objects. The proposed approach is compared to 7 other PDE techniques, presenting its robustness and reliability, without creating a grainy effect around edges.

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Texture removal by pixel classification using a rotating filter

Cited by 11 publications

References 12 publications

Perceptual Color Image Smoothing via a New Region-Based PDE Scheme

Perceptual Color Image Smoothing via a New Region-Based PDE Scheme

Perceptual curve extraction

Derivative Half Gaussian Kernels and Shock Filter

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